Overlapping community detection in social networks with Voronoi and tolerance neighborhood-based method
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Community detection is typically viewed as a graph clustering problem with early detection algorithms focused on detecting non-overlapping communities and formulating various measures and optimization methods to evaluate the quality of clustering. In recent years, overlapping community detection especially in real-world social networks, has become an important and challenging research area since it introduces the possibility of membership of a vertex in more that one community. Overlapping community detection by its definition implies soft clustering and leads to an ideal application of granular computing methods. In this paper, a hybrid computational geometry approach with Voronoi diagrams and tolerance-based neighborhoods (VTNM) is used to detect overlapping communities in social networks. Voronoi partitioning results in a crisp partition of an Euclidean space and a tolerance relation makes it possible to obtain soft partitions. A Voronoi diagram is a method to partition a plane into regions based on nearness to points in a specific set of sites (seeds). In the VTNM approach, these seeds are used as cores for determining tolerance neighborhoods via a non-transitive binary relation. The intersection of these neighborhoods are used to discover overlapping communities. Our proposed VTNM algorithm was tested with 7 small real-world networks and compared with 11 well-known algorithms. VTNM algorithm shows promising results in terms of the Extended Modularity measure, Average F1-score and Normalized Mutual Information (NMI) measure.
KeywordsCommunity detection Granular computing Near sets Social networks analysis Tolerance neighborhoods Voronoi diagrams
This research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant # 194376 and is supported by the Queen Elizabeth II Diamond Jubilee scholarship.
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